A Hybrid Gene Selection Techniques for Cancer Classification - Anidha, M;Premalatha, K
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Due to the high dimensionality of microarray gene expression data, feature selection is an important task which improves the performance of the classification in the area of cancer diagnosis. In this article, innovative methods are proposed for selecting highly informative gene subsets of gene expression data that effectively classify the cancer data into tumorous and non-tumorous. A novel technique to identify signature biomarkers is also discussed.

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Produktbeschreibung
Due to the high dimensionality of microarray gene expression data, feature selection is an important task which improves the performance of the classification in the area of cancer diagnosis. In this article, innovative methods are proposed for selecting highly informative gene subsets of gene expression data that effectively classify the cancer data into tumorous and non-tumorous. A novel technique to identify signature biomarkers is also discussed.
Autorenporträt
M. Anidha is full-time scholar research scholar in Anna University Chennai. Her area of research includes Bioinformatics and Data mining.Dr. K. Premalatha is currently working as a Professor in Bannari Amman Institute of Technology, Erode. Her area of research includes, Soft computing, Data mining and Bioinformatics